Free Energy Perturbation Method Research Articles

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State.

We critically assess the capabilities of classical density functional theory (DFT) based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state to predict the solvation free energies of small molecules in various hydrocarbon solvents. We compare DFT results with experimental data from the Minnesota solvation database and utilize statistical methods to analyze the accuracy of our approach, as well as its weaknesses. The mean absolute error of the solvation free energies is 3.7 kJ mol-1 for n-alkane solvents, ranging from pentane to hexadecane, with 473 solute-solvent systems. For solvents consisting of cyclic hydrocarbons (cyclohexane, benzene, toluene, and ethylbenzene) with 245 solute-solvent systems, we report a slightly larger mean absolute error of 4.2 kJ mol-1. We identify three possible sources of errors: (i) the neglect of solute-solvent and solvent-solvent Coulomb interactions, which limits the applicability of PC-SAFT DFT to nonpolar and weakly polar molecules; (ii) the solute's Lennard-Jones parameters supplied by the general AMBER force field, which are not parametrized toward solvation free energies; and (iii) the application of the Lorentz-Berthelot combining rules to the dispersive interactions between a segment of the PC-SAFT solvent and a Lennard-Jones interaction site of the solute. The approach is more accurate than standard implementations of phenomenological models in common chemistry software packages, which exhibit mean absolute errors larger than 9.12 kJ mol-1, even though newer phenomenological models achieve a mean absolute error of about 2 kJ mol-1. PC-SAFT DFT is more computationally efficient than state of the art explicit molecular simulations in combination with free energy perturbation methods. It is predictive with respect to solvation free energies, i.e., the input for the model is the (element-specific) molecular force field, the solute configuration from molecular dynamics simulations, and the (substance-specific) PC-SAFT parameters. The PC-SAFT parametrization uses pure-component data and does not require experimental solvation free energies. The PC-SAFT equation of state, without applying a DFT formalism, can also be used to calculate solvation free energies, provided that the PC-SAFT parameters for the solute are available. A large number of substances was recently parametrized by members of our group (Esper, T.; Bauer, G.; Rehner, P.; Gross, J. Ind. Eng. Chem. Res. 2023, 62), which enables a comparison to the DFT approach for 103 substances. Accurate results are obtained from the PC-SAFT equation of state with an MAE below 2.51 kJ mol-1. The DFT approach does not require PC-SAFT parameters for the solute and can be applied to all solutes that can be represented by the molecular force field.

Molecular Dynamics Simulations on Spike Protein Mutants Binding with Human β Defensin Type 2.

Human β defensin type 2 (hBD-2), a cationic cysteine-rich peptide secreted from the human innate immune system, can bind Spike-RBD at the same site as receptor ACE2, thus blocking viral entry into ACE2-expressing cells. In order to find out the impact of CoV-2 mutations on hBD-2's antiviral activity, it is important to investigate the binding and interaction of hBD-2 with RBD mutants. All-atom molecular dynamics simulations were conducted on typical RBD mutants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N, and N501Y-E484K-K417N, binding with hBD-2. Starting from the stable binding structure of hBD-2 and wt-RBD and ClusPro and HADDOCK docking-predicted initial structures, the RBD variants bound with hBD-2 simulations were set up, and NAMD simulations were conducted. Based on the structure and dynamics analysis, it was found that most RBD variants can still form a similar number of hydrogen bonds with hBD-2, in addition to having a similar-sized buried surface area (BSA) and a similar binding interface to the RBD wildtype. However, the RBD triple mutant formed a less stable binding structure with hBD-2 than other variants. Additionally, the free energy perturbation (FEP) method was applied to calculate the contribution of key mutant residues to the binding and the free energy change caused by the mutations. The result shows that N439K, K417N, and the trimutation increase the binding free energy of RBD with hBD-2; thus, RBD should bind less stably with hBD-2. E484K decreases the binding free energy, thus it should bind more stably with hBD-2, while N501Y, S477N, T478I, and P479S almost do not change the binding free energy with hBD-2. The MM-GBSA method predicted the binding interaction energy which shows that the trimutant should be able to escape the binding with hBD-2 but N501Y should not. The result can provide insight into understanding the functional mechanism of hBD-2 combating SARS-CoV-2 mutants.

Development of a QM/MM(ABEEM) method for the deprotonation of neutral and cation radicals in the G-tetrad and GGX(8-oxo-G) tetrad.

The rapid deprotonation of G˙+ in the DNA strand impedes positive charge (hole) transfer, whereas the slow deprotonation rate of G˙+ in the G-tetrad makes it a more suitable carrier for hole conduction. The QM/MM(ABEEM) combined method, which involves the integration of QM and the ABEEM polarizable force field (ABEEM PFF), was developed to investigate the deprotonation of neutral and cation free radicals in the G-tetrad and GGX(8-oxo-G) tetrad (xanthine and 8-oxoguanine dual substituted G-tetrad). By incorporating valence-state electronegativity piecewise functions χ*(r) and implementing charge local conservation conditions, QM/MM(ABEEM) possesses the advantage of accurately simulating charge transfer and polarization effect during deprotonation. The activation energy calculated by the QM method of X˙ is the lowest among other bases in the GGX(8-oxo-G) tetrad, which is supported by the computation of the average electronegativity calculated by ABEEM PFF. By utilizing QM/MM(ABEEM) with a two-way free energy perturbation method, the deprotonation activation energy of X˙ in the GGX(8-oxo-G) tetrad is determined to be 33.0 ± 2.1 kJ mol-1, while that of G˙+ in the G-tetrad is 20.7 ± 0.6 kJ mol-1, consistent with the experimental measurement of 20 ± 1.0 kJ mol-1. These results manifest that X˙ in the GGX(8-oxo-G) tetrad exhibits a slower deprotonation rate than G˙+ in the G-tetrad, suggesting that the GGX(8-oxo-G) tetrad may serve as a more favorable hole transport carrier. Furthermore, the unequal average electronegativities of bases in the GGX(8-oxo-G) tetrad impede the deprotonation rate. This study provides a potential foundation for investigating the microscopic mechanism of DNA electronic devices.

Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

For an effective drug, strong binding to the target protein is a prerequisite, but it is not enough. To produce a particular functional response, drugs need to either block the proteins' functions or modulate their activities by changing their conformational equilibrium. The binding free energy of a compound to its target is routinely calculated, but the timescales for the protein conformational changes are prohibitively long to be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that the binding free energies of the ligands with different receptor conformations may infer their efficacy. However, this hypothesis has not been thoroughly validated. We present an actionable protocol and a comprehensive study to show that binding thermodynamics provides a strong predictor of the efficacy of a ligand. We apply the absolute binding free energy perturbation method to ligands bound to active and inactive states of eight G protein-coupled receptors and a nuclear receptor and then compare the resulting binding free energies. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state. Our method achieves unprecedented performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.

Accurately Predicting Protein pKa Values Using Nonequilibrium Alchemy.

The stability, solubility, and function of a protein depend on both its net charge and the protonation states of its individual residues. pKa is a measure of the tendency for a given residue to (de)protonate at a specific pH. Although pKa values can be resolved experimentally, theory and computation provide a compelling alternative. To this end, we assess the applicability of a nonequilibrium (NEQ) alchemical free energy method to the problem of pKa prediction. On a data set of 144 residues that span 13 proteins, we report an average unsigned error of 0.77 ± 0.09, 0.69 ± 0.09, and 0.52 ± 0.04 pK for aspartate, glutamate, and lysine, respectively. This is comparable to current state-of-the-art predictors and the accuracy recently reached using free energy perturbation methods (e.g., FEP+). Moreover, we demonstrate that our open-source, pmx-based approach can accurately resolve the pKa values of coupled residues and observe a substantial performance disparity associated with the lysine partial charges in Amber14SB/Amber99SB*-ILDN, for which an underused fix already exists.

Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor.

The ongoing COVID-19 pandemic continues to infect people worldwide, and the virus continues to evolve in significant ways which can pose challenges to the efficiency of available vaccines and therapeutic drugs and cause future pandemic. Therefore, it is important to investigate the binding and interaction of ACE2 with different RBD variants. A comparative study using all-atom MD simulations was conducted on ACE2 binding with 8 different RBD variants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N and N501Y-E484K-K417N on RBD. Based on the RMSD, RMSF, and DSSP results, overall the binding of RBD variants with ACE2 is stable, and the secondary structure of RBD and ACE2 are consistent after the point mutation. Besides that, a similar buried surface area, a consistent binding interface and a similar amount of hydrogen bonds formed between RBD and ACE2 although the exact residue pairs on the binding interface were modified. The change of binding free energy from point mutation was predicted using the free energy perturbation (FEP) method. It is found that N501Y, N439K, and K417N can strengthen the binding of RBD with ACE2, while E484K and P479S weaken the binding, and S477N and T478I have negligible effect on the binding. Point mutations modified the dynamic correlation of residues in RBD based on the dihedral angle covariance matrix calculation. Doing dynamic network analysis, a common intrinsic network community extending from the tail of RBD to central, then to the binding interface region was found, which could communicate the dynamics in the binding interface region to the tail thus to the other sections of S protein. The result can supply unique methodology and molecular insight on studying the molecular structure and dynamics of possible future pandemics and design novel drugs.

Computer-aided drug design approaches applied to screen natural product’s structural analogs targeting arginase in Leishmania spp

Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics–generalized Born surface area (MM–GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors.

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔGbind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R2 ≥ 0.5) with experimental values of ΔGbind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Computer-aided drug design approaches applied toscreen natural product's structural analogs targeting arginase in Leishmania spp.

Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics-generalized Born surface area (MM-GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions by MM-GBSA and FEP methods. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.

Deciphering the Anomalous Acidic Tendency of Terminal Water at Rutile(110)–Water Interfaces

Understanding the mechanism of the oxygen evolution reaction (OER) is essential to improve the efficiency of photocatalysis for TiO2. Previous studies have highlighted the importance of terminal hydroxide radical (TiOH•) in the OER. Ab initio molecular dynamics simulations (AIMD) with hybrid functional have revealed that this radical readily loses its proton, creating the key intermediate, oxygen radical anion (Ti5cO•–). Herein, we combine machine-learning-accelerated molecular dynamics with density functional theory calculations to demonstrate that the Ti5cO•– can alternatively be generated through the trapping of a hole in a terminal oxygen anion (Ti5cO2–) at rutile(110)–water interfaces. Further examination reveals that the Ti5cO2– results from the deprotonation of Ti5cOH– and remains stable at the charge-neutral interfaces for a transient time period of ca. 100 ps. The AIMD-based free energy perturbation method predicts that the acidity constant of Ti5cOH– is even smaller than that of Ti5cOH2, thereby rationalizing the stability of Ti5cO2–. Structural analyses show that this anomalous acidic tendency of terminal water originates from the decrease of Ti–O bond length and the transition of Titanium’s coordination from octahedral to pyramidal in Ti5cO2–. Our findings provide valuable insights into the surface acid–base chemistry and a potential explanation for the pH-dependent behavior of photogenerated holes for TiO2.

Topical selective TYK2 inhibition attenuates inflammation and clinical symptoms in a mouse model of human psoriasis

Abstract Psoriasis is a chronic skin condition, that affects ~8M patients in the United States. A majority (~7M) of patients suffer from mild to moderate form of disease that lacks safe & effective treatment options. Although antibodies targeting Th1/Th17 cytokines (eg IL12/IL23/IL17) are highly effective in moderate to severe psoriasis, they are not prescribed to mild to moderate patients due to high costs & safety concerns. There is a huge unmet need for developing safe and effective non-steroidal topical therapies. Small molecule inhibitors of Th1/Th17 pathways with optimal topical drug like properties can effectively address patient needs; making TYK2 inhibitors (TYK2i) attractive candidates for treating mild to moderate psoriasis. However, given the high degree of structural similarity among JAK family, designing topical selective TYK2i have remained a challenge. Using an innovative structure based approach, we have designed, synthesized and characterized TYK2i optimized for topical drug like properties and JAK family selectivity using free energy perturbation (FEP) methods and medchem SAR. We have identified TYK2i with pM to low nM potencies that are highly selective for TYK2 among 468 kinases. These analogs have enhanced topical bioavailability with suitable drug like properties. In a mouse model of psoriasis, these analogs dose-dependently suppressed PASI clinical scores, ear & back skin thickness and inflammation. Additionally, skin histology showed significant attenuation of epidermal thickening and pSTAT activation suggesting that topical TYK2 inhibition retains anti-inflammatory activity while eliminating potential for dose-limiting side effects observed with systemic non-selective TYK2 or other JAK inhibitors. This research has been fully funded by Alembic Pharmaceuticals Ltd., including salary support to all authors.

Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy.

We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.

Aptamer affinity to P53 DBD: A molecular dynamics study

Protein P53, as a tumor suppressor plays an important role in many biological processes. Its DNA binding domain (DBD) contains a Zn ion which has an important role in aggregation and binding. The presence of abnormal wild-type P53 and its mutations in high concentration can be indicative of a pathological cancer state. Because of the intrinsic disordering in its structure, finding structures to overcome disordering and to interact with this protein and its mutations is one of the challenges of scientists for using P53 as a biomarker to early diagnosis of cancers. Here, we used the molecular dynamics simulation to study the interaction of wild-type P53 DBD and its mutations with an aptamer to find one solution for the mentioned challenge. The analyses of RMSD, Electrostatic energy, Number of hydrogen binding are used to discuss the quality of interactions. Aptamer- protein binding free energy is calculated with free energy perturbation method and value of ΔG= -181 Kcal.mol−1 for the interaction of aptamer- R249S, allow us to introduce the aptamer as a material to capture and recognize of R249S.

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures.

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a model based on low prior structural information. In order to address this, we have developed an AlphaFold2 version where we exclude all structural templates with more than 30% sequence identity from the model-building process. In a previous study, we used those models in conjunction with state-of-the-art free energy perturbation methods and demonstrated that it is possible to obtain quantitatively accurate results. In this work, we focus on using these structures in rigid receptor-ligand docking studies. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario for virtual screening campaigns; in fact, we strongly recommend to include some post-processing modeling to drive the binding site into a more realistic holo model.

Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors.

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (ΔΔGRBFEA→B) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.

Alchemical free energy to probe RNA/ligand interactions.

Alchemical free energy calculations are increasingly being applied in academic research as well as in the pharmaceutical industry. These methods originate in theories of Kirkwood and Zwanzig, where Kirkwood introduced a coupling parameter approach, and Zwanzig showed that the free energy difference between two states can be computed via an appropriate exponential average of energy differences over an ensemble of configurations. In recent years, alchemical free energy calculations have benefited from improved force fields, new sampling algorithms, and the emergence of parallel computing, which have been shown to yield accurate results. These calculations are routinely applied to large-scale free energy calculations for ligand modification, and for testing mutations in amino acids and nucleotides. Here, we report alchemical calculations on RNA/peptide and RNA/small-molecule complexes, which are conducted using free energy perturbation (FEP) and thermodynamic integration (TI) methods. We discovered that the peptides employ base-specific hydrogen bonding, sugar-phosphate backbone interactions, and cation-pi interactions for the recognition of RNA molecules. In contrast, the small molecules recognize the RNA via base-specific and pi-pi stacking interactions.

The role of the membrane in driving CLC-ec1 dimerization.

Most membrane proteins oligomerize in the membrane. In the case of the bacterial chloride/proton antiporter CLC-ec1, the role of highly stable homodimers is not understood since individual CLC-ec1 protomers can maintain the chloride/proton transport stoichiometry. In a previous study, we used experimental and computational approaches to show that in C16:0/C18:1 palmitoyl-oleoyl lipid membranes, the monomeric state of CLC-ec1 creates a membrane defect that is ≈ 8 Å thinner than the bulk. However, in the dimer state, the membrane is restored to a near-native bilayer thickness and structure. We hence proposed that the dimerization is driven by the larger free-energy cost of lipid solvation of the dissociated monomers. Here, we carry out coarse-grained molecular dynamics simulations with umbrella sampling and free energy perturbation methods to quantify the membrane's contribution to free energy of dimerization. In the case of CLC-ec1, addition of 30% C12:0 di-lauryl chain lipids stabilizes the free energy of the monomeric state by ≈ 2 kcal/mole, in qualitative agreement with our experimental observations. Therefore, we have a physical model of how changes in lipid composition can regulate self-assembly reactions of membrane proteins in membranes. This work was partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-840782.

MemCross: Accelerated Weight Histogram method to assess membrane permeability

Passive permeation events across biological membranes are determining steps in the pharmacokinetics of xenobiotics. To reach an accurate and rapid prediction of membrane permeation coefficients of drugs is a complex challenge, which can efficiently support drug discovery. Such predictions are indeed highly valuable as they may guide the selection of potential leads with optimum bioavailabilities prior to synthesis. Theoretical models exist to predict these coefficients. Many of them are based on molecular dynamics (MD) simulations, which allow calculation of permeation coefficients through the evaluation of both the potential of mean force (PMF) and the diffusivity profiles. However, these simulations still require intensive computational efforts, and novel methodologies should be developed and benchmarked. Free energy perturbation (FEP) method was recently shown to estimate PMF with a significantly reduced computational cost compared to the adaptive biasing force method. This benchmarking was achieved with small molecules, namely short-chain alcohols. Here, we show that to estimate the PMF of bulkier, drug-like xenobiotics, conformational sampling is a critical issue. To reach a sufficient sampling with FEP calculations requires a relatively long time-scale, which can lower the benefits related to the computational gain. In the present work, the Accelerated Weight Histogram (AWH) method was employed for the first time in all-atom membrane models. The AWH-based protocol, named MemCross, appears affordable to estimate PMF profiles of a series of drug-like xenobiotics, compared to other enhanced sampling methods. The continuous exploration of the crossing pathway by MemCross also allows modeling subdiffusion by computing fractional diffusivity profiles. The method is also versatile as its input parameters are largely insensitive to the molecule properties. It also ensures a detailed description of the molecule orientations along the permeation pathway, picturing all intermolecular interactions at an atomic resolution. Here, MemCross was applied on a series of 12 xenobiotics, including four weak acids, and a coherent structure-activity relationship was established.

Identifying pathways to metal-organic framework collapse during solvent activation with molecular simulations.

Metal-organic framework (MOF) materials are a vast family of nanoporous solids with potential applications ranging from drug delivery to environmental remediation. Application of MOFs in these scenarios is hindered, however, by difficulties in MOF 'activation' after initial synthesis - removal of the synthesis solvent from the pores to make the pore space accessible - often leading to framework collapse if improperly performed. While experimental studies have correlated collapse to specific solvent properties and conditions, the mechanism of activation-collapse is currently unknown. Developing this understanding would enable researchers to create better activation protocols for MOFs, accelerating discovery and process intensification. To achieve this goal, we simulated solvent removal using grand-canonical Monte Carlo and free energy perturbation methods. By framing activation as a fluid desorption problem, we investigated activation processes in the isoreticular metal organic framework (IRMOF) family of MOFs for different solvents. We identified two pathways for solvent activation - the solvent either desorbs uniformly from each individual pore or forms coexisting phases during desorption. These mesophases in turn lead to large capillary stresses within the framework, corroborating experimental hypotheses for the cause of activation-collapse. Finally, we found that the activation energy of solvent removal increased with pore size and connectivity due to the increased stability of solvent mesophases, matching experimental findings. Using these simulations, it is possible to screen MOF activation procedures, enabling rapid identification of ideal solvents and conditions and thus enabling faster development of MOFs for practical applications.